CN109238271B - Line fitting method based on time - Google Patents
Line fitting method based on time Download PDFInfo
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- CN109238271B CN109238271B CN201810966746.6A CN201810966746A CN109238271B CN 109238271 B CN109238271 B CN 109238271B CN 201810966746 A CN201810966746 A CN 201810966746A CN 109238271 B CN109238271 B CN 109238271B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
Abstract
The invention relates to a time-based line fitting method, which clusters position information, links screened central track points according to time, calculates related clustering parameters meeting scene requirements from the angle of speed data of the track points, and improves the flexibility of line fitting. The invention solves the line fitting problem based on time information, and can extract a typical data set, thereby fitting a trajectory line containing less data, and having high analysis efficiency, clear route and stable clustering result.
Description
Technical Field
The invention belongs to the technical field of earth observation application, and relates to a time-based line fitting method.
Background
Under general conditions, units participating in crowdsourcing can send position information to a crowdsourcing platform through a smart phone, a Beidou and other mobile devices capable of sending signals, and the crowdsourcing platform can analyze a fitting route which can be referred by researchers according to an algorithm model after receiving signal data. In the geographic information system, technicians need to judge whether sailing behaviors of ships and airplanes in a sea area are out of range or not and whether relevant regulations are violated or not according to the flight paths, and an effective mode is adopted to avoid repeated occurrence of the events. If the huge big dipper position information is simply linked up according to the time, the trajectory line can be messy, and certain burden is brought to the analysis work of technicians. Therefore, how to filter out redundant Beidou point location information is one of the problems to be solved by the invention. If people want to research the travel track of people in one area, some behavior habits of people are summarized, and more convenient service is provided for people. Then, it would be a great advance in intelligent transportation. However, the travel behavior of people is unpredictable, for example, when people encounter an acquainted person from the way of a home unit, the person stays in a certain area for a long time, and from the data collected in real time, the area generates a large amount of redundant point location information, and certainly, the person may search for lost objects and go round in a certain area, so that the track fitted according to the point location data collected in real time is messy and is not beneficial to summarizing the behavior habits of people. For this problem, many existing processing methods, such as mean clustering, need to select a proper classification number, and if density clustering analysis is used, density radius and trajectory density need to be determined, which greatly limits the adaptability of the algorithm. This limitation can be solved to some extent well by the idea of vector quantization. A typical example is the encoding of images.
The existing track analysis methods generally need to manually set parameters to adapt to specific situations, so that designing a simple and effective route fitting scheme is very important. Zahedeh IZakian et al propose a trajectory data automatic clustering technique [1] based on particle swarm optimization, and consider dynamic time warping distance as one of the most common distance measures of trajectory data. For example, experts in traffic control and city planning are interested in the movement patterns of vehicles (e.g., cars, buses, airplanes) at different time intervals. They can use this information for road construction or design monitoring systems, etc. This all represents the superiority of the automatic clustering technique, but their approach does not make the trajectory data set compact. In addition, a plurality of existing methods for processing position data by using an R tree exist, for example, a large-scale crowdsourcing task management method [2] based on R tree space camouflage proposed by Y Li et al, SJ Lee et al make research on a massive ship position data analysis system constructed by applying a quadtree and an R-tree [3], and the time information of a track point is not considered in the position information analysis based on the R tree, so that the dynamics of the track line cannot be reflected. The invention relates to a line fitting method which is a simple line fitting method based on time information and designed by using a vector quantization idea for reference.
[1]Izakian Z, Mesgari M S, Abraham A. Automated clustering oftrajectory data using a particle swarm optimization[J]. Computers,Environment and Urban Systems, 2016, 55: 55-65；
[2]Li Y, Shin B S. Task-Management Method Using R-Tree SpatialCloaking for Large-Scale Crowdsourcing[J]. Symmetry, 2017, 9(12): 311；
[3]Lee S J, Park G K, Kim D Y. Study on applying Quad-Tree&R-Treefor building the analysis system using massive ship position data[J]. Journalof Korean Institute of Intelligent Systems, 2011, 21(6): 698-704。
Disclosure of Invention
The invention aims to overcome the problems in the prior art, provides a time-based line fitting method, extracts representative position information, and solves the problem of line fitting based on time information.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a method of time-based line fitting, the method comprising the steps of:
step 1) in a data collection stage, a crowdsourcing system issues a task request, a user unit provides position information after receiving the task request and sends the position information to the crowdsourcing system in real time, and the crowdsourcing system screens out invalid position information which is not regular;
step 2) data analysis, wherein position information sent by each user unit is provided with a category number, and after a task request is finished, a crowdsourcing system classifies and analyzes point location information according to the category number;
and 3) line fitting, namely drawing grids to cover all position information through vector quantization or a direct mode, clustering a point set in each grid, screening representative points in each grid, and finally connecting the representative points in each grid according to time to form a track line.
Further, in the step 1), each user unit participating in the task request of the crowdsourcing system is provided with a corresponding point location sending device, and a corresponding transmitting frequency f is set, wherein the transmitting frequency f is set according to different traffic devices.
Further, in step 1), step 2) and step 3), the position information at least includes position, time and speed data.
Further, in the step 3), a large amount of static velocity data is removed by adopting K-means clustering, so as to calculate related clustering parameters meeting the requirement of the situation, calculate a central point of each grid as a representative point, and finally join the central point set according to the time data of the central point to form a final trajectory line.
Further, in the step 3), when the grid is drawn to cover all the position information by using vector quantization, the size of the unit grid
As a vector quantization of the pixel size
The velocity data of the point is
，
Represents the set of all the point locations and,
clustering the velocity data according to the mean value for unit time
Dividing the cluster into two clusters, taking the average speed of the cluster with higher speed as the basis for calculating the grid size, thereby obtaining the size
Is defined as follows:
wherein
For the set of all tracing points, i.e. the size of the grid
The flexibility of the line fitting is increased in dependence on the velocity data determination of the trajectory.
Further, in step 3), when a direct clustering mode is adopted, determining the clustering number, wherein the clustering number is determined by counting the grid number covering the track points, and if the grid number with the point location information is
Of 1 at
The grid position of the point is
The number of clusters is
Then the number of non-empty grids can be obtained
Comprises the following steps:
wherein the number of clusters
Get
The scale value of (a) is used as a reference clustering parameter.
Go toStep 3), when the central point of each grid is calculated by adopting K-means clustering, each pixel point is taken as data, and the K-means clustering is executed to generate
The pixel values of all the points in the corresponding cluster are then replaced by the pixel values of these center points.
The invention has the beneficial effects that:
1. the invention can extract a typical data set so as to fit a trace line containing a small amount of data. Generally, the clustering method first needs to preset the number of clusters or the cluster density, and then setting a suitable clustering parameter is a core element for determining the clustering result. In most cases, the track is fluctuant, and the adaptability of the line fitting device can be better expanded by automatically calculating reasonable clustering parameters according to specific conditions.
2. The states of a large amount of position information can be judged through a small number of position point sets, the analysis efficiency is improved, and a clear route is beneficial to research and work of people. For example, whether the ship resides in a certain sea area for a long time or whether the ship wants to know the traveling habits of a person, the result can be analyzed by only a few point sets.
3. The invention can avoid unstable clustering results caused by unreasonable clustering parameter setting, and the clustering scheme based on vector quantization can better give consideration to discrete points, while the scheme based on direct clustering is simpler.
Drawings
FIG. 1 is a diagram of a crowdsourcing system of the present invention;
FIG. 2 is a graph of a line fitting analysis of the present invention;
FIG. 3a is a first fitting trajectory graph obtained by the direct clustering method and the vector quantization method, respectively, according to the present invention;
FIG. 3b is a second fitting trajectory graph obtained by the direct clustering method and the vector quantization method, respectively, according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
A method of time-based line fitting, the method comprising the steps of:
step 1) in a data collection stage, a crowdsourcing system issues a task request, a user unit provides position information after receiving the task request and sends the position information to the crowdsourcing system in real time, and the crowdsourcing system screens out invalid position information which is not regular;
step 2) data analysis, wherein position information sent by each user unit is provided with a category number, and after a task request is finished, a crowdsourcing system classifies and analyzes point location information according to the category number;
and 3) line fitting, namely drawing grids to cover all position information through vector quantization or a direct mode, clustering a point set in each grid, screening representative points in each grid, and finally connecting the representative points in each grid according to time to form a track line.
Further, in the step 1), each user unit participating in the task request of the crowdsourcing system is provided with a corresponding point location sending device, and a corresponding transmitting frequency f is set, wherein the transmitting frequency f is set according to different traffic devices.
Further, in step 1), step 2) and step 3), the position information at least includes position, time and speed data.
Further, in the step 3), a large amount of static velocity data is removed by adopting K-means clustering, so as to calculate related clustering parameters meeting the requirement of the situation, calculate a central point of each grid as a representative point, and finally join the central point set according to the time data of the central point to form a final trajectory line.
Further, in the step 3), when the grid is drawn to cover all the position information by using vector quantization, the size of the unit grid
As a vector-quantized pixel size,first, the
The velocity data of the point is
，
Represents the set of all the point locations and,
clustering the velocity data according to the mean value for unit time
Dividing the cluster into two clusters, taking the average speed of the cluster with higher speed as the basis for calculating the grid size, thereby obtaining the size
Is defined as follows:
wherein
For the set of all tracing points, i.e. the size of the grid
The flexibility of the line fitting is increased in dependence on the velocity data determination of the trajectory.
Further, in step 3), when a direct clustering mode is adopted, determining the clustering number, wherein the clustering number is determined by counting the grid number covering the track points, and if the grid number with the point location information is
Of 1 at
The grid position of the point is
The number of clusters is
Then the number of non-empty grids can be obtained
Comprises the following steps:
wherein the number of clusters
Get
The scale value of (a) is used as a reference clustering parameter.
Further, in the step 3), when the center point of each grid is calculated by adopting K-means clustering, each pixel point is taken as data, and the K-means clustering is executed to generate
Center point (i.e. number of clusters)
) The pixel values of all the points in the corresponding cluster are then replaced by the pixel values of these center points.
In the present examples, the present invention is explained in detail below:
the first part
FIG. 1 is a system diagram of location information data collection. The crowdsourcing system comprises two steps, a data collection phase and a data parsing phase. The method comprises the steps that firstly, a crowdsourcing platform issues a task request, the masses provide position information after receiving the request, then the crowdsourcing platform screens out irregular noise data, each unit or individual participating in crowdsourcing needs to be provided with corresponding point location sending equipment, corresponding transmitting frequency f is set, and the frequency f can be set according to different traffic equipment.
Fig. 2 is a technical analysis diagram of line fitting. And the information sent by each unit is provided with a class number, and after the task is finished, the platform classifies and analyzes the point location information according to the class number. Covering all position information by using the thought of vector quantization, then carrying out K-means clustering operation on the point sets in each grid, calculating a central point, and finally connecting the central point sets according to the time information of the central point to form a final trajectory line.
The second part
The following is a detailed description of the implementation of the technical solution of the present invention and the scientific principles underlying it.
The line fitting method based on time mainly includes the idea of clustering, however, the clustering operation needs preset related parameters, such as cluster number, density radius, cluster density and other information. In order to increase flexibility, in this embodiment, a clustering parameter that meets the situation is calculated by using the speed data of the point location and a grid covering method.
Assuming a unit cell size of
Of 1 at
The velocity data of the point is
，
Represents the set of all the point locations and,
is unit time, then according to the idea of mean value clustering, the speed data is dividedThe method is divided into two clusters, and the average speed of the cluster with higher speed is taken as the basis for calculating the grid size, because a large number of point locations close to zero speed have little significance for analyzing dynamic track information. The following definitions can thus be obtained:
definition 1 (i.e. vector quantized pixel size)
)：
In this way, the size of the grid can be determined from the velocity data of the trajectory, thereby increasing the flexibility of the line fitting method. If the line fitting method adopts a direct clustering scheme, the clustering number is related, the clustering number can be determined by counting the number of grids covering the track points, and the number of grids with point position information is assumed to be
Of 1 at
The grid position of the point is
The number of clusters is
Then the following reasoning can be drawn:
lemma 1 (i.e., number of non-empty grids)
)：
Number of clusters
Get
As a reference clustering parameter, e.g.
，
，
Etc., an excessively small number of clusters may cause an unstable situation of clustering to occur.
In consideration of the important significance of some discrete track points, the track data is simplified by using the idea of a picture compression technology, namely a vector quantization method. For a gray picture, a compression scheme using a clustering method to select representative points is undoubtedly a better scheme. The specific process is as follows: using each pixel point as a data, executing K-means, and generating
The pixel values of all the points in the corresponding cluster are then replaced by the pixel values of these center points. The concept of vector quantization can also be applied to the design of line trajectory fitting, for example, a line point set can be simulated as a black pixel in a gray picture, so that a representative point set of a line can be extracted by simulating a picture compression method.
Third part
The line fitting method based on time comprises the following specific implementation steps:
the crowdsourcing system issues a demand, a user provides position information after receiving the demand and sends the position information to the crowdsourcing system in real time, and the crowdsourcing system carries out next-step analysis according to collected data information such as position, time and speed.
By defining 1 and lemma 1, the number of direct clusters of position data and the grid size of vector quantization can be obtained. The dashed line parts in fig. 3a and 3b can be obtained by using a direct clustering method, and the solid line parts in fig. 3a and 3b can be obtained by using a vector quantization method, wherein the position data adopts simulation data which is increased randomly.
As shown by dotted lines in fig. 3a and 3b, the track point set is divided into a plurality of cluster clusters according to reasonable clustering density and clustering radius, then the central point of each cluster is taken to represent the representative point of the region, and then all the cluster central points are connected together according to time information to form a new track line.
The solid line part in fig. 3a and 3b shows a line fitting scheme based on the idea of vector quantization, and we can calculate the size of vector quantization pixels, i.e. the size of the grid in fig. 3, then perform K-means clustering on the point set in each grid, take out the central point, and finally connect all the central points according to time to generate a reduced version curve, by definition 1.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method of time-based line fitting, the method comprising the steps of:
step 1) in a data collection stage, a crowdsourcing system issues a task request, a user unit provides position information after receiving the task request and sends the position information to the crowdsourcing system in real time, and the crowdsourcing system screens out invalid position information which is not regular;
step 2) data analysis, wherein position information sent by each user unit is provided with a category number, and after a task request is finished, a crowdsourcing system classifies and analyzes point location information according to the category number;
step 3) line fitting, wherein grids are drawn to cover all position information through vector quantization or a direct mode, then a point set in each grid is clustered, representative points in each grid are screened out, and finally the representative points in each grid are connected together according to time to form a track line;
in the step 3), when the vector quantization is adopted to draw the grid to cover all the position information, the size of the unit grid
As a vector quantization of the pixel size
The velocity data of the point is
，
Represents the set of all the point locations and,
clustering the velocity data according to the mean value for unit time
Dividing the cluster into two clusters, taking the average speed of the cluster with higher speed as the basis for calculating the grid size, thereby obtaining the size
Is defined as follows:
in the step 3), when a direct clustering mode is adopted, the clustering number is determined firstly, the clustering number is determined by counting the grid number covering the track points, and if the grid number with the point position information is
Of 1 at
The grid position of the point is
The number of clusters is
Then the number of non-empty grids can be obtained
Comprises the following steps:
2. The time-based line fitting method according to claim 1, wherein in step 1), each user unit participating in the task request of the crowdsourcing system is equipped with a corresponding point location sending device, and sets a corresponding transmission frequency f, and the transmission frequency f is set according to different traffic devices.
3. The time-based line fitting method according to claim 1, wherein in step 1), step 2) and step 3), the position information comprises at least position, time and speed data.
4. The time-based route fitting method according to claim 3, wherein in the step 3), K-means clustering is adopted to remove a large amount of stationary velocity data, so as to calculate related clustering parameters meeting the requirement of the scene, the central point of each grid is calculated as a representative point, and finally the central point set is connected according to the time data of the central point to form a final trajectory.
5. The time-based line fitting method according to claim 1, wherein in the step 3), when the center point of each grid is calculated by adopting K-means clustering, each pixel point is taken as data, and the K-means clustering is performed to generate
The pixel values of these center points are then used to replace the pixel values of all the points in the corresponding cluster.
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